Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic

Abstract

Objective This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019. Materials and Methods We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital’s COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. Results Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. Conclusion Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions.

Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures

“…Reviewed articles mentioned several limitations of EHR log research ( Supplementary eTable 4 ). Three of the most frequently mentioned limitations echo those observed in the prior review of audit log research 11 : EHR logs do not provide a full view of clinical activity which can involve physical and digital interactions outside the EHR (22 articles), 3 , 26 , 34 , 43 , 55 , 57 , 58 , 61 , 65 , 68 , 73 , 74 , 84–86 , 89 , 91 , 92 , 95 , 103 , 109 , 112 qualitative methods are needed to better understand the context and motivation for observed work (15 articles), 32 , 46 , 53 , 62 , 64 , 68 , 72 , 80 , 85 , 91 , 96 , 97 , 101 , 115 , 118 and logs may not contain enough detail to observe complex workflows (13 articles). 4 , 26 , 32 , 43 , 51 , 56 , 59 , 69 , 75 , 81 , 89 , 90 ,…”

“… 78–109 Studies employing vendor- and investigator-derived measures also included different participants ( P < .001). While vendor-measure studies only ever included physicians or advanced practice providers (APPs), a third of investigator-measure studies (34%) included all EHR users who performed the observed activity, 73–76 , 84–90 , 103–108 , 115–118 and another 11% specifically included nurses, medical students, or scribes. 77 , 91–95 , 109 Vendor-measure studies were more likely to include data from multiple institutions (25% vs 8% of studies, P = .025) and to observe overall EHR use, rather than only collect data on a specific activity such as note writing or inbox management (98% vs 48% of studies, P < .001).…”

“… 97 Three observational studies predicted patient-provider relationships based on EHR use. 82 , 98 , 103 Ten observational studies were longitudinal, comparing EHR use at different points in time including before and after the start of the coronavirus disease 2019 (COVID-19) pandemic, 2 , 3 , 34 , 37 , 89 before and after a change to the EHR interface, 107 before and after policy changes, 101 , 109 and at different points of clinical training. 32 , 52 The 11 experimental studies examined the impact of scribes, 30 , 41 , 111 EHR training, 38 , 45 , 54 , 119 and interface changes on EHR use.…”

“…Eight articles reported the results of measure validation, with no difference in reporting between vendor-measure and investigator-measure studies (10% vs 5%, P = .473). 6 , 53 , 79 , 80 , 83 , 85 , 102 , 103 These articles compared resident duty hours derived from EHR logs with self-reported or Global Positioning System tracked hours (3 articles), 80 , 83 , 102 patient-clinician relationships derived from logs with those from COVID-19 contact tracers or self-report (2 articles), 85 , 103 EHR time derived from logs with self-report (2 articles), 6 , 53 and EHR login time derived from logs with those observed through screen recording (1 article). 79 Five additional articles referenced previous validations for measures of EHR time including articles referencing Epic’s, 28 Cerner’s, 29 Arndt et al’s, 66 , 124 and “other EHR vendors” methods.…”

Healthcare personnel interactive pathogen exposure response system

“…As a result, the median list length of HCP identified through clinical data was significantly larger than conventional processes (82 vs 10). To combat the potential problem of overnotification, which has been noted in other EHR-based exposure investigations, 9 we created a “contact score” that estimated the risk of exposure based on time and type of activity. Our comparison to conventional exposure investigations showed that all HCP who tested positive were above the median of contact scores (Fig.…”